A qualitative characterization of meso-activity factors to estimate soil exposure for agricultural workers

Abstract

Background: Agricultural workers' exposure to soil contaminants is not well characterized. Activity pattern data are a useful exposure assessment tool to estimate extent of soil contact, though existing data do not sufficiently capture the range and magnitude of soil contact in the agricultural context.

Objective: We introduce meso-activity, or specific tasks, to improve traditional activity pattern methodology. We propose a conceptual framework to organize the factors that may modify soil exposure and impact soil contact estimates within each meso-activity in agriculture. We build upon models from the US EPA to demonstrate an application of this framework to dose estimation.

Methods: We conducted in-depth interviews with sixteen fruit and vegetable growers in Maryland to characterize factors that influence soil exposure in agriculture. For illustrative purposes, we demonstrate the application of the framework to translate our qualitative data into quantitative estimates of soil contact using US EPA models for ingestion and dermal exposure.

Results: Growers discussed six tasks, or meso-activities, involving interaction with soil and described ten factors that may impact the frequency, duration and intensity of soil contact. We organized these factors into four categories (i.e., Environmental, Activity, Timing and Receptor; EAT-R) and developed a framework to improve agricultural exposure estimation and guide future research. Using information from the interviews, we estimated average daily doses for several agricultural exposure scenarios. We demonstrated how the integration of EAT-R qualitative factors into quantitative tools for exposure assessment produce more rigorous estimates of exposure that better capture the true variability in agricultural work.

Significance: Our study demonstrates how a meso-activity-centered framework can be used to refine estimates of exposure for agricultural workers. This framework will support the improvement of indirect exposure assessment tools (e.g., surveys and questionnaires) and inform more comprehensive and appropriate direct observation approaches to derive quantitative estimations of soil exposure.

Impact statement: We propose a novel classification of activity pattern data that links macro and micro-activities through the quantification and characterization of meso-activities and demonstrate how the application of our qualitative framework improves soil exposure estimation for agricultural workers. These methodological advances may inform a more rigorous approach to the evaluation of pesticide and other chemical and biological exposures incurred by persons engaged in the cultivation of agricultural commodities in soil.

Keywords: Agriculture; Behavior; Macro-activity; Meso-activity; Soil ingestion; Time-activity patterns.

Fig. 1. Summary of activity data types (macro- meso- and micro) and examples related to soil exposure estimation for agricultural context.

This figure summarizes the types of activity-pattern data within the agricultural context. The top row defines farming as a macro-activity. The middle row defines examples of six tasks or meso-activities that emerged from in-depth interviews (IDIs). The bottom row contains examples of micro-activities described directly by growers in IDIs.

Fig. 2. Environmental, Activity, Timing – Receptor (EAT-R) Framework describing factors impacting soil exposure in the agricultural context.

This figure summarizes the environmental, activity, timing and receptor factors that may impact soil exposure in the agricultural context. The bold text indicates the four broad categories of factors identified via interviews with growers. The plain text identifies the ten sub-factors that impact soil exposures. The arrows indicate the direction of potential influences of factors on each other.